Self-management strategies in chronic obstructive pulmonary disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Self-management has gained increased relevance in the management of chronic obstructive pulmonary disease patients. The heterogeneity in self-management interventions has complicated the development of recommendations for clinical practice. In this review, we present the latest findings regarding conceptual definition, effectiveness of self-management interventions and self-management strategies in chronic obstructive pulmonary disease as a first step toward personalized medicine: what, how and to whom? RECENT FINDINGS: Self-management interventions have shown benefits in improving health-related quality of life and reducing hospital admissions. Favorable outcomes can only be achieved if patients have an ultimate goal, that is their desired achievements in their life. In the continuum of care, the components of the self-management program will vary to adapt to the condition of the patient (disease severity, comorbidities) and to factors such as patient motivation, confidence (self-efficacy), access to health care, family and social support. A combination of education, case management and patient-centric action plan has shown the best chance of success. SUMMARY: The individual patient's needs, own preferences and personal goals should inform the design of any intervention with a behavioral component. A continuous loop process has to be implemented to constantly assess what work and does not work, aiming at achieving the desired outcomes for a given patient.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".